Europlanet Science Congress 2020
Virtual meeting
21 September – 9 October 2020
Europlanet Science Congress 2020
Virtual meeting
21 September – 9 October 2020
EPSC Abstracts
Vol.14, EPSC2020-36, 2020
https://doi.org/10.5194/epsc2020-36
Europlanet Science Congress 2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Machine learning classification of new asteroid families members

Valerio Carruba
Valerio Carruba
  • UNESP, School of Natural Sciences and Engineering, Mathematics, Guaratinguetá, Brazil (valerio.carruba@unesp.br)

Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object.  These groups are mainly identified in proper elements or frequencies domains.   Because of robotic telescope surveys, the number of known asteroids has increased from about 10,000 in the early 90's to more than 750,000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may   struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a,e,sin(i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand alone and ensemble approaches.  The Extremely Randomized Trees (ExtraTree) method had the highest precision, enabling to  retrieve up to 97% of family members identified with standard HCM.

How to cite: Carruba, V.: Machine learning classification of new asteroid families members, Europlanet Science Congress 2020, online, 21 September–9 Oct 2020, EPSC2020-36, https://doi.org/10.5194/epsc2020-36, 2020